Machine learning surrogate modeling toward the design of lattice-structured heat sinks fabricated by additive manufacturing

Abstract

Metal additive manufacturing (AM) enables the fabrication of lattice-structured heat sinks with enhanced heat transfer properties. The lattice structural features for enhancing heat transfer and suppressing pressure loss need to be clarified. To describe the structural features dominating heat transfer and pressure loss, eleven structural parameters consisting of three groups, namely the area, hydraulic diameter, and effective flow path, are proposed and used as inputs for the neural network (NN) surrogate model. The surrogate model precisely and quickly predicts the heat transfer property and pressure loss simulated by computational fluid dynamics. The random forest feature importance is used to select necessary and sufficient structural features. Four parameters describing the bottleneck in the flow pathway, surface area, and intricate structures are selected. Even when only the selected four parameters are used, the NN model maintains a high prediction performance, suggesting that the four parameters are sufficient for describing the heat transfer and pressure loss. Four parameters are used to discuss the design concept for lattice-structured heat sinks. An appropriately intricate lattice structure exhibits improved heat transfer and reduced pressure loss compared to a simple lattice structure. This study provides new insights into the design of lattice-structured heat sinks fabricated by AM.